1. Field of the Invention
The present invention relates to the field of digital signal processing. More particularly, the present invention relates to a method and apparatus for use in acoustic feedback suppression in digital audio devices such as hearing aids.
2. Background
Acoustic feedback, which is most readily perceived as high-pitched whistling or howling, is a persistent and annoying problem typical of audio devices with relatively high-gain settings, such as many types of hearing aids. FIG. 1 is a system model of a prior art hearing aid. The prior art hearing aid model 100 shown in FIG. 1 includes a digital sample input sequence X(n) 110 which is added to a feedback output 125 to form a signal 127 that is processed by hearing loss compensation function G(Z) 130 to form a digital sample input sequence Y(n) 140. As shown in FIG. 1, acoustic leakage (represented by transfer function F(Z) 150) from the receiver to the microphone in a typical hearing aid makes the hearing aid act as a closed loop system. Feedback oscillations occur when the gain G(Z) is increased to a point which makes the system unstable. As known to those skilled in the art, to avoid acoustic feedback oscillations, the gain of the hearing aid must be limited to this point. As a direct result of this limitation, many hearing impaired individuals cannot obtain their prescribed target gains, and low-intensity speech signals remain below their threshold of audibility. Furthermore, even when the gain of the hearing aid is reduced enough to avoid instability, sub-oscillatory feedback interferes with the input signal X(n) and causes the gain of the feedforward transfer function Y(Z)/X(Z) to not be equal to G(z). For some frequencies, Y(Z)/X(Z) is much less than G(z) and will not amplify the speech signals above the threshold of audibility.
Prior art feedback cancellation approaches for acoustic feedback control either typically use the compensated speech signals (i.e., Y(n) 140 in FIG. 1), or add a white noise probe as the input signal to the adaptive filter.
Wideband feedback cancellation approaches without a noise probe are based on the architecture shown in FIG. 2, where like components are designated by like numerals. As shown in the adaptive feedback cancellation system 100 of FIG. 2, a delay 170 is introduced between the output 140 and the feedback path 150. In addition, a wideband feedback cancellation function W(Z) 160 is provided at the output of delay 170, and the output of the wideband feedback cancellation function W(Z) 160 is subtracted from the input sequence X(n) 110. The wideband feedback cancellation function W(Z) 160 is controlled by error signal e(n) 190, which is the result of subtracting the output of the wideband feedback cancellation function W(Z) 160 from the input sequence X(n) 110. Although the technique illustrated in FIG. 2 may sometimes provide an additional 6-10 dB of gain, the recursive nature of this configuration can cause the adaptive filter to diverge. Alternatively, adaptive filtering in the subbands requires fewer taps, operates at a much lower rate, and converges faster in some cases. Moreover, feedback cancellation in the frequency domain seems to work even better than in the subbands. Those skilled in the art understand that some frequency domain cancellations scheme will allow for a 20 dB increase in the stable gain of a behind-the-ear (xe2x80x9cBTExe2x80x9d) hearing aid device without feedback or noticeable distortion. However such frequency domain schemes require the additional complexity of a Fast Fourier Transform (xe2x80x9cFFTxe2x80x9d) and an Inverse Fast Fourier Transform (xe2x80x9cIFFTxe2x80x9d) in both the forward path and the feedback prediction path.
Feedback cancellation methods using a noise probe are dichotomized based on the control of their adaptation as being either continuous or noncontinuous. FIG. 3 is a block diagram of a prior art continuous adaptive feedback cancellation system 300 with noise probes. As shown in FIG. 3, a noise source N 310 injects noise to the output 315 of the hearing loss compensation function G(Z) 130 at a summing junction 320. The block diagram of a continuous-adaptation feedback cancellation system shown in FIG. 3 may increase the stable gain by 10-15 dB. However, the overriding disadvantage of such a system is that the probe noise is annoying and reduces the intelligibility of the processed speech. Alternatively, in the noncontinuous-adaptation feedback cancellation system illustrated in FIG. 4, the normal signal path is broken and the noise probe 310 is only connected during adaptation. Adaptation is triggered only when certain predetermined conditions are met. However, it is very difficult to design a decision rule triggering adaptation without introducing distortion or annoying noise.
A different feedback cancellation apparatus and method has been recently proposed, comprising a feedback canceller with a cascade of two wideband filters in the cancellation path. This method involves using linear prediction to determine Infinite Impulse Response (xe2x80x9cIIRxe2x80x9d) filter coefficients which model the resonant electro-acoustic feedback path. As known to those skilled in the art, linear prediction is most widely used in the coding of speech, where the IIR-filter coefficients model the resonances of the vocal tract. In this system, the IIR filter coefficients are estimated prior to normal use of the hearing aid and are used to define one of the cascaded wideband filters. The other wideband filter is a Finite Impulse Response (xe2x80x9cFIRxe2x80x9d) filter, and adapts during normal operation of the hearing aid.
A new subband feedback cancellation scheme is proposed, capable of providing additional stable gain without introducing audible artifacts. The subband feedback cancellation scheme employs a cascade of two narrow-band filters Ai(Z) and Bi(Z) along with a fixed delay, instead of a single filter Wi(Z) and a delay to represent the feedback path in each subband. The first filter, Ai(Z), is called the training filter, and models the static portion of the feedback path in ith subband, including microphone, receiver, ear canal resonance, and other relatively static parameters. The training filter can be implemented as a FIR filter or as an IIR filter. The second filter, Bi(Z), is called a tracking filter and is typically implemented as a FIR filter with fewer taps than the training filter. This second filter tracks the variations of the feedback path in the ith subband caused by jaw movement or objects close to the ears of the user.